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Customer Information Management

Learn how to discover relationships across multiple categories of data to gain a better understanding of customer behaviour.

Recently, I watched an athletics track meet and one of the races prompted a thought about customer experience:

Middle distance sprinters will start at different points on the track, follow their own lane, but ultimately end up in the same place. That’s a lot like customers taking their own journeys – and it’s making a difference in the way we need to organise information.

When the customer journey was largely predictable, brands could target a marketing campaign to a segment of prospects on a certain day after showing interest, or have sales get in touch after a certain number of emails had gone out. But that experience is no longer a straight line (or a single lane) – it’s a maze with multiple entrances. Now, customers are approximately 57 percent through a purchase decision before they even contact a supplier. They’re better informed and doing a lot more research before committing to any big purchases.

It used to be ok to have customer information stored by department, captured and managed by each as a prospect progressed through their journey to become a valued customer. Often, the data did not travel outside the team using it – resulting in siloes of campaign data, product information, purchasing history, and other customer data. Siloed systems keep data locked in place, making it difficult to tell where customers have been and where they’re going next.

With the advent of the connected customer, that rigid system of data management is coming under heavy pressure to change. Customers drop in an out of buying cycles at different points using different channels, and occasionally in different countries. In a race, this would be like watching the 800 meter with some athletes joining at the 400-meter mark and others at the 200-meter mark.

To reinvent the customer experience by using data in new ways, organisations need to rethink how information is stored and used. For customer data, master data management solutions also need to evolve and organise information into a new model: a knowledge graph.

A New Kind of Journey

The more-informed, more-connected consumer is constantly on the move and quickly develops opinions about products and services. For companies to craft a message that hits home, the message has to be sent at the right time, through the right channel, and with the right context. A knowledge graph breaks down departmental siloes, taking all the information about customers, transactions, and products and integrating it, building a model that’s enriched with new, unstructured information from third parties such as social media networks. With this broader understanding of the customer, communications can be far more focused and relevant.

For example, if a company is looking to target prospects based on previous purchases, it’s possible to add the context of sentiment, available from social media posts. Someone who is already discussing the topic on Twitter could be a better qualified lead for a sales call, while someone who has been searching key terms around the topic could be better qualified for an early stage marketing campaign.

The Future of Customer Experience

With customers more informed than ever before and joining the race at different times, the future of customer experience is context. In a world where customers can take so many different paths to a purchase, businesses need to create a knowledge graph data model that allows for a more complete view of complex customer needs.

Revamping data management practices and solutions to fit this new model may sound complicated, but, in the end, it will drastically simplify customer information management by creating a consolidated, reliable source of data that can viewed at any angle and used by any department.